WTD-PSD: Presentation of Novel Feature Extraction Method Based on Discrete Wavelet Transformation and Time-Dependent Power Spectrum Descriptors for Diagnosis of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a type of dementia that affects the elderly population. A machine learning (ML) system has been trained to recognize particular patterns to diagnose AD using an algorithm in an ML system. As a result, developing a feature extraction approach is critical for reducing calculation time. The input image in this article is a Two-Dimensional Discrete Wavelet (2D-DWT). The Time-Dependent Power Spectrum Descriptors (TD-PSD) model is used to represent the subbanded wavelet coefficients. The principal property vector is made up of the characteristics of the TD-PSD model. Based on classification algorithms, the collected characteristics are applied independently to present AD classifications. The categorization is used to determine the kind of tumor. The TD-PSD method was used to extract wavelet subbands features from three sets of test samples: moderate cognitive impairment (MCI), AD, and healthy controls (HC). The outcomes of three modes of classic classification methods, including KNN, SVM, Decision Tree, and LDA approaches, are documented, as well as the final feature employed in each. Finally, we show the CNN architecture for AD patient classification. Output assessment is used to show the results. Other techniques are outperformed by the given CNN and DT.

Tourist Attraction Recommendation Method and Data Management Based on Big Data Analysis

“…e tourist industry is now thriving, and the government is increasingly appreciating its contribution to economic growth and job creation. Many challenges in the growth of tourism can be solved with the advancement of computer arti cial intelligence technologies [2][3][4][5][6][7]. When planning a trip, people have a lot of choices to make, including where they want to go, how long they want to spend there, and what form of transportation they want to use.…”

“…Researchers and people in charge of making business decisions are becoming increasingly interested in big data. To name a few, the tourism sector has shown a keen interest in topics like tourist destination (TD) tactical thinking, tourism management, relationship management, and even destination marketing [5][6][7]14]. In despite the fact that digital networks have indeed been acknowledged as a helpful and dependable resource of information for travelers [8], it is still in its infancy in the analysis of large data created specifically via social media, especially in the domain of tourism management.…”

Oral Cancer at a Glance

“…By following these guidelines, dentists can help protect their patients' oral health during cancer treatment. And other diseases, such as types of cancer, respiratory diseases and Alzheimer's, it is important to check the condition of the disease [63][64][65][66][67].…”